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Abstract Soybean oil, medium-chain triglycerides, olive oil, and fish oil (SMOFlipid) is used without evidence of benefits. We investigated the relationship between lipid emulsions and brain injury in term-equivalent age magnetic resonance imaging (MRI) in 148 very preterm infants with a birth weight of

Abstract Background Status epilepticus (SE) is a top neurological and medical emergency. Adequate staff knowledge and sufficient hospital resources are mandatory for timely management and better outcomes. This study aims to evaluate Palestinian ER doctors’ knowledge and beliefs towards SE and assess the state and availability of hospital resources needed to manage SE appropriately. Methods This cross-sectional study surveyed all ER doctors working in the six governmental hospitals in the Gaza Strip during June 2022. A questionnaire assessed the knowledge and beliefs toward SE and the practice of SE knowledge acquisition. Simultaneous field surveys determined the availability of essential medications and resources at each hospital and doctors’ actual awareness about the status of these resources. Linear regression determined predictors of higher knowledge scores. Results 137 doctors participated (response rate 81%). Most were males, had graduated within six years of data collection, and 45.3% worked at Shifa Medical Complex. 63.5% of participants recognized the correct operational definition of status epilepticus. 85.4 and 83.9% recognized that it might cause permanent brain damage or death, respectively. Only 10.9% recognized the average mortality rate of a single convulsive SE episode. 44.5% believed that awaiting spontaneous SE resolution is a valid approach to management. Significant defects were noted in the awareness of subclinical SE, including its recognition and prevalence in ciritcally ill patients. Having a higher qualification and graduating from local medical schools predicted higher scores. In field surveys, EEG was unavailable at all hospitals, while CT scans and a neurologist physician were unavailable at two hospitals. IV phenytoin was the only second-line treatment available at all hospitals. IV valproate and levetiracetam were universally unavailable. Conclusions The survey revealed significant defects in Palestinian ER doctors’ knowledge and beliefs towards SE. Several hospitals also demonstrated a critical lack of resources to manage this condition appropriately. This should be addressed by the local health authorities to avoid potentially preventable SE-related mortality and morbidity.

Multiple myeloma is a hematological malignancy characterized by the clonal proliferation of plasma cells. Recent advancements in treatment strategies have led to a significant shift in the management of this malignancy. Venetoclax, an apoptosis inducer, has emerged as a promising therapeutic option for multiple myeloma. Fatty acid oxidation (FAO) is one of the metabolic reprogramming events that occurs because of mutations in certain genes and thus plays a crucial role in cancer progression. Teglicar, a reversible inhibitor of FAO, targets carnitine palmitoyl transferase 1 (CPT1) and has shown potential in cancer treatment due to its interaction with apoptotic regulators such as Bcl-2. In this study we investigated the in vitro anti-cancer activity of teglicar, in myeloma cell lines (RPMI 8226 and U266B1). Our findings revealed a significant inhibition of RPMI 8226 cell viability with an IC50 = 50 µM, but at a higher concentration for U266B1 cells. Notably, the combination of teglicar with venetoclax showed a synergistic effect. Although both cell lines were known to be resistant to venetoclax, teglicar significantly reduced IC50 of venetoclax by 5.33 and 1.7 folds in RPMI 8226 and U266B1 respectively. In addition, single agent and combination treatment were associated with a significant increase in apoptosis in both cell lines. However, the combination therapy had no significant effect on reactive oxygen species (ROS) levels in either cell lines. Our results suggest that the combination of teglicar and venetoclax holds promise in multiple myeloma through induction of apoptosis. This study sheds light on the potential therapeutic implications of targeting FAO, specifically through CPT1 inhibition, in conjunction with apoptosis inducers for enhanced anticancer effects in multiple myeloma.

Abstract Background Neuronal ceroid lipofuscinosis (NCL) is a heterogeneous group of 13 rare, progressive neurodegenerative diseases of the brain and retina. CLN14 is a very rare subtype of NCL caused by pathogenic variants in the KCTD7 gene. Only four cases of this subtype have been reported in the literature. Case presentation A nine-month-old, previously healthy male who was firstborn to first-cousin parents presented with progressive psychomotor regression, dysmorphic facial features, myoclonus, and vision loss. Neurological examination showed generalized hypotonia and brisk reflexes. He continued to deteriorate until age 18 months, when he developed his first generalized tonic-clonic seizure. An ophthalmological examination showed a hypopigmented fundus and slight temporal disc pallor. Brain MRI showed mild generalized brain atrophy and white matter disease. EEG revealed a severely abnormal trace marked by generalized, high amplitude, sharply contoured, polymorphic delta slowing intermixed with theta slowing and some alpha activity, with disorganized and scattered spikes and sharp waves. The patient continued to have uncontrolled seizures and further psychomotor regression until he died of status epilepticus and pneumonia at the age of 44 months. WES identified a novel homozygous variant c.413T > C, p.(Leu138Pro) in the KCTD7 gene, causing an amino acid transition from leucine to proline at position 138. Both parents were carriers of the same variant. Conclusions We present the fifth known case of CLN14 in the literature and report the clinical course and a novel underlying likely causative variant in the KCTD7 gene. The improving accessibility and affordability of genetic testing will likely uncover more NCL cases and further expand the disease’s genotypic and phenotypic spectrum.

Abstract Background Mental illness is highly stigmatized compared to physical illness, often due to false beliefs and a lack of knowledge. Negative attitudes harbored by healthcare providers reflect on the care they provide and impact their patients’ outcomes and may tailor their future preferences away from the psychiatric practice. This cross-sectional study used a stratified, randomized sampling method to survey the beliefs and attitudes of students at a Palestinian medical school toward mental illness using the Beliefs Toward Mental Illness (BMI) scale. Results Overall, 79.1% of 282 participants demonstrated positive attitudes toward mentally ill patients (BMI score

In response to the reform in the Malaysian market that requires all publicly listed companies (PLCs) to have at least one female director on the board, this study examines how female directors’ attributes (FDAs) shape integrated reporting quality (IRQ) and assesses the potential influence of substantial shareholders on the FDA–IRQ nexus. Using all Malaysian PLCs implementing the integrated reporting from 2017 to 2021, our analysis yields four key findings. First, a positive relationship exists between the presence of a Muslim female director and IRQ. Second, female directors with financial expertise and those with higher education positively impact IRQ. Third, the holding of multiple directorships by female directors is negatively linked to IRQ, and the length of their tenure shows no significant effect. Fourth, the relationship between FDAs and IRQ is more pronounced in contexts with a greater ownership concentration. These results remain robust after rigorous analysis and mitigating for potential biases. Our findings offer valuable insights for policymakers, practitioners, and researchers interested in understanding the implications of gender diversity and IRQ in emerging markets.

Abstract This study examines the relationship between family-controlled companies and corporate social responsibility practice disclosure (CSRPD) and examines whether board independence influences this relationship. A self-constructed CSR disclosure index is developed to measure the CSRPD in a sample of 152 publicly listed companies in Saudi Arabia from 2016 to 2021. The findings from the pooled ordinary least squares (POLS) regression reveal that family-controlled companies exhibit lower levels of CSRPD than non-family companies. However, family-controlled companies with a higher number of independent directors on their boards show higher CSRPD, indicating a significant positive interaction effect of board independence. These results remain robust even after applying a variety of econometric techniques, including Newey-West regression, panel corrected standard error (PCSE), logistic regression, and addressing endogeneity problems, along with using different measures for CSRPD and family-controlled companies. These findings suggest that the governance structure of the boards, particularly independent directors, can support the prosocial and positive stimulus of socioemotional wealth (SEW) theory. Therefore, Saudi Arabian capital market regulators need to be aware of the importance of companies’ governance structures.

Abstract Permanent magnet synchronous motor (PMSM) systems have gained popularity in various fields due to their advantages such as high speed, high accuracy, low maintenance, and high reliability. This paper presents the speed tracking control of a permanent magnet synchronous motor (PMSM) using a hybrid fractional order PI and type 2 fuzzy control with fractional order PD control (FOT2F-FOPD). The SRF-PLL observes the motor speed and estimates the rotor's position by interpreting the input voltages of the motor instead of using a sensor. Then, the controller parameters (gain, μ and λ) are tuned based on a novel optimization algorithm called Incomprehensible but Intelligible-in-time (IbI) Logics algorithm (ILA). The proposed controller enhances the performance of the system and regulates the speed of the motor under parameter variations such as the speed and the load. So, the proposed ILA (FOT2F-FOPD) controller is assessed using MATLAB/Simulink simulation and compared with other controller techniques. The proposed technique reduces the settling time, steady state error and overshoot by at least 65%, 54% and 53% respectively under load conditions compared with (PSO, optimized FOPD, FOPI and PI). While at no load condition, the settling time and the error are reduced by 31% and 12.5% respectively with no overshoot in output response. The results show a significant improvement in the performance of motors used with the application of the proposed controller and the employment of the (ILA) optimization compared with FOPI and PI controllers.

AimsPatients with severe aortic stenosis (AS), low transvalvular flow (LF) and low gradient (LG) with normal ejection fraction (EF)—are referred to as paradoxical LF-LG AS (PLF-LG). PLF-LG patients develop more advanced heart failure symptoms and have a worse prognosis than patients with normal EF and high-gradient AS (NEF-HG). Despite its clinical relevance, the mechanisms underlying PLF-LG are still poorly understood.MethodsLeft ventricular (LV) myocardial biopsies of PLF-LG (n = 5) and NEF-HG patients (n = 6), obtained during transcatheter aortic valve implantation, were analyzed by LC-MS/MS after sequential extraction of cellular and extracellular matrix (ECM) proteins using a three-step extraction method. Proteomic data are available via ProteomeXchange with identifier PXD055391.Results73 cellular proteins were differentially abundant between the 2 groups. Among these, a network of proteins related to muscle contraction and arrhythmogenic cardiomyopathy (e.g., cTnI, FKBP1A and CACNA2D1) was found in PLF-LG. Extracellularly, upregulated proteins in PLF-LG were related to ATP synthesis and oxidative phosphorylation (e.g., ATP5PF, COX5B and UQCRB). Interestingly, we observed a 1.3-fold increase in cyclophilin A (CyPA), proinflammatory cytokine, in the extracellular extracts of PLF-LG AS patients (p

In this paper, an improvement for the ϕ6-model expansion method is presented. In this approach, contrary to the classical ϕ6-model expansion method, obtaining explicit solutions for nonlinear ordinary and partial differential equations is congenial and undemanding of any constraint conditions, where the method can be applied and used for obtaining solutions without having any conditions on them. Moreover, the new approach is used to obtain new solutions for the new (3+1)-dimensional integrable Kadomtsev–Petviashvili equation. We demonstrated that for the same equation, the classical ϕ6-model expansion and the improved ϕ6-model expansion approaches produce the same family of solutions. However, the improved ϕ6-model expansion method is found to be more efficient and convenient.

Abstract Due to vincristine sulfate’s (VCR sulfate) toxicity and non-specific targeting, which might adversely damage healthy cells, its clinical application is restricted. In this study, we loaded VCR sulfate on exosomes generated from mesenchymal stem cells (MSCs) to enhance its targeted distribution. Exosomes are able to deliver molecules to specific cells and tissues and have therapeutic potential. In this study, we isolated exosomes from MSCs, and using probe-sonication approach loaded them with VCR sulfate. Using SRB assay, the cytotoxicity of VCR sulfate-Exo was assessed in T47D breast cancer cells, and the results were contrasted with those of free VCR sulfate. Then We labeled markers (CD44+/CD24−) in the cell line to assess the targeting effectiveness of VCR sulfate-Exo using flow cytometry. Our results showed that the cytotoxicity of VCR sulfate-Exo was nearly the same as that of VCR sulfate. Flow cytometry analysis revealed that VRC sulfate-Exo was more effectively targeted to MSCs than free VCR sulfate. Our study shows that loading VCR sulfate to MSCs-derived exosomes can improve their targeted delivery and lessen their side effects. Additional research is required to determine VCR sulfate-Exo’s in vivo effectiveness and safety and improve the loading and delivery strategies.

Abstract Background Febrile neutropenia (FN) is a life-threatening complication of cancer therapy. Appropriate antibiotic treatment improves the clinical outcome in these patients; however, the increasing rate of anti-microbial resistance makes its therapy particularly challenging. Aim This study aims to investigate the microbial spectrum and antimicrobial resistance pattern in cancer patients with FN at King Abdullah University Hospital, Jordan. Method Blood cultures of 261 FN patients pre-diagnosed with malignancy (age 1–18 years) were enrolled in this study. Results The most common isolated microorganisms were gram-positive bacteria (50.2℅). Gram-infections with coagulase-negative Staphylococcus (CONS) are the most prevalent pathogens, followed by gram-negative infections with Klebseilla pneumonia and fungal infections with nonalbicans strains. All CONS, Methicillin-resistant Staphylococcus aureus (MRSA), and enterococcus species were sensitive to Vancomycin and Teicoplanin. Ten percent of the gram-negative organisms were Extended-spectrum beta-lactamase (ESBL) and all were sensitive to carbapenems. 66.7% of pseudomonas aeruginosa blood cultures were sensitive to Piperacillin-Tazobactam and 83.4% were sensitive to carbapenems. All Enterobacter species were sensitive to Carbapenems. Conclusion Isolates showed various antibiotic sensitivity and resistance patterns; therefore, a judicious management plan is essential to establish an appropriate and effective institutional policy for the use of empirical antibiotics in patients of FN.

AbstractWith the increasing demand for greater financial and sustainability reporting transparency, firms globally have embraced integrated reporting (IR). However, little is known about how audit committee effectiveness (ACE) affects IR quality and whether family ownership moderates this relationship. This study aims to address this research gap by examining the impact of ACE on IR quality in the Malaysian market. In addition, the study further examined the moderating role of family ownership on this relationship. Data are extracted from firms’ annual reports and Thomson Reuters DataStream to analyse Malaysian firms spanning the period 2017–2021. Our findings indicate that ACE positively influences IR quality, fostering more transparent disclosure. Additionally, our analysis reveals a negative moderation effect by family ownership on the ACE-IR quality nexus. Further scrutiny of a sub-sample suggests a positive ACE—IR quality relationship in firms without family ownership, contrasting with a negative relationship in those with family ownership. Our results withstand alternative measures of IR, ACE, estimation techniques, and control for endogeneity issues. This research contributes to the literature on IR by adding new insights into the impact of ACE and family ownership on IR quality and provides important implications for regulators, stakeholders, researchers, managers, and investors.

Multiple peptide resistance factor (MprF) confers resistance to cationic antimicrobial peptides (AMPs) in several pathogens, thereby enabling evasion of the host immune response. The role of MprF in commensals remains, however, uncharacterized. To close this knowledge gap, we used a common gut commensal of animals, Lactiplantibacillus plantarum, and its natural host, the fruit fly Drosophila melanogaster, as an experimental model to investigate the role of MprF in commensal-host interactions. The L. plantarum ΔmprF mutant that we generated exhibited deficiency in the synthesis of lysyl-phosphatidylglycerol (Lys-PG), resulting in increased negative cell surface charge and increased susceptibility to AMPs. Susceptibility to AMPs had no effect on ΔmprF mutant's ability to colonize guts of uninfected flies. However, we observed significantly reduced abundance of the ΔmprF mutant after infection-induced inflammation in the guts of wild-type flies but not of flies lacking AMPs. Additionally, we found that the ΔmprF mutant compared to wild-type L. plantarum induces a stronger intestinal immune response in flies due to the increased release of immunostimulatory peptidoglycan fragments, indicating an important role of MprF in promoting host tolerance to commensals. Our further analysis suggests that MprF-mediated lipoteichoic acid modifications are involved in host immunomodulation. Overall, our results demonstrate that MprF, besides its well-characterized role in pathogen immune evasion and virulence, is also an important commensal resilience factor.

Digitization of healthcare data has shown an urgent necessity to deal with privacy concerns within the field of deep learning for healthcare organizations. A promising approach is federated transfer learning, enabling medical institutions to train deep learning models collaboratively through sharing model parameters rather than raw data. The objective of this research is to improve the current privacy-preserving federated transfer learning systems that use medical data by implementing homomorphic encryption utilizing PYthon for Homomorphic Encryption Libraries (PYFHEL). The study leverages a federated transfer learning model to classify cardiac arrhythmia. The procedure begins by converting raw Electrocardiogram (ECG) scans into 2-D ECG images. Then, these images are split and fed into the local models for extracting features and complex patterns through a finetuned ResNet50V2 pre-trained model. Optimization techniques, including real-time augmentation and balancing, are also applied to maximize model performance. Deep learning models can be vulnerable to privacy attacks that aim to access sensitive data. By encrypting only model parameters, the Cheon-Kim-Kim-Song (CKKS) homomorphic scheme protects deep learning models from adversary attacks and prevents sensitive raw data sharing. The aggregator uses a secure federated averaging method that averages encrypted parameters to provide a global model protecting users’ privacy. The system achieved an accuracy rate of 84.49% when evaluated using the MIT-BIH arrhythmia dataset. Furthermore, other comprehensive performance metrics were computed to gain deeper insights, including a precision of 72.84%, recall of 51.88%, and an F1-score of 55.13%, reflecting a better understanding of the adopted framework. Our findings indicate that employing the CKKS encryption scheme in a federated environment with transfer cutting-edge technology achieves relatively high accuracy but at the cost of other performance metrics, which is lower in the encrypted settings when compared to the plain one, an acceptable trade-off to ensure data privacy through encryption with achieving an optimal model performance.

Abstract: This study focuses on exploring the potential of artificial intelligence as an alternative, effective, and user-preferred tool for answering inquiries, compared to traditional methods such as FAQs or email and ticketing systems. The study highlights how AI can enhance efficiency and accuracy in processing and responding to inquiries from readers, authors, and reviewers, by providing immediate and customized answers based on the analysis of information available on the journal's website and the data fed to the chatbot. Through in-depth discussions and an analysis of the inquiries received over a full six months, totaling about 3000 inquiries, the study demonstrates the good ability of the chatbot to understand complex inquiries and provide satisfactory answers. The study indicates that chatbots can reduce the workload on editorial teams of scientific journals by automating responses to routine inquiries, allowing staff to dedicate more time to editorial and academic tasks. One of the key aspects of training is teaching the chatbot to provide correct answers to various inquiries and to avoid responding to negative or redundant inquiries. The research explores the challenges of applying AI in this context, including the need to train smart models to understand specific academic language and ensure accuracy in responses, as well as addressing privacy concerns and data security. The importance of designing flexible and adaptable AI systems to meet the diverse requirements of different scientific journals and their users is emphasized. The study concludes that artificial intelligence is a promising tool for improving the interaction between academic journals and their communities, offering an effective alternative to traditional systems. It highlights the necessity for ongoing research and development to enhance AI capabilities. Notably, the AI tool currently lacks a direct method for correcting its wrong answers, which is one of the most effective learning tools used by parents to correct their children's answers. One of the key recommendations of the study is that AI training should be conducted in stages.

Stock market prediction poses significant challenges due to the inherent noise and volatility of the data. These challenges are further amplified in emerging stock markets, where data volatility increases due to numerous endogenous and exogenous variables. Despite the progress made in models for stock market prediction, such as Autoregressive Integrated Moving Average (ARIMA), Support Vector Machines (SVMs), and deep learning models, there is still a need for further research in emerging stock markets. This study addresses the complexity and non-linearity of emerging stock market data by proposing a deep learning model that utilizes Gated Recurrent Unit (GRU) algorithm to predict the next-day closing price. The proposed model leverages the inclusion of exogenous variables to enhance the model’s performance. Three datasets are constructed for three main emerging market indices, specifically in Qatar, Saudi Arabia, and China. Using mean absolute percentage error (MAPE), the inclusion of exogenous variables led to a noticeable improvement over the related work results from 0.74, 1.68, and 0.72 for indices of Qatar, Saudi Arabia, and China respectively to 0.16, 0.6, and 0.2. Furthermore, the results demonstrate the appropriateness of GRU algorithm for predicting emerging stock markets.

A common model for classifying images is the convolutional neural network (CNN), which has the benefit of effectively using data correlation information. Despite their remarkable success, classical CNNs may face challenges in achieving further improvements in accuracy, computational efficiency, explainability, and generalization. However, if the specified data dimension or model grows too large, CNN becomes difficult to train effectively with a slowdown processing. In order to address a problem using CNN utilizing quantum computing, Quantum Convolutional Neural Network (QCNN) proposes a novel quantum solution or enhances the functionality of an existing learning model in terms of processing time during training. This paper presents a comparative analysis between classical Convolutional Neural Networks (CNNs) and a novel quantum circuit architecture tailored for image-based tasks, emphasizing the adaptability and versatility of quantum circuits in enhancing feature extraction capabilities and then final accuracy and processing time. A MNIST and covidx-cxr3 datasets was used to train quantum-CNN models, and the results of these comparisons were made with traditional CNN performance. The results demonstrate that the suggested QCNN beat the traditional CNN in terms of recognition accuracy and processing speed (process time) when combined with cutting-edge feature extraction techniques. This superiority is particularly evident when trained on the covidx-cxr3 dataset, highlighting the potential for quantum computing to revolutionize image classification tasks.

Abstract This paper introduces a novel approach aimed at enhancing the control performance of a specific class of unknown multiple-input and multiple-output nonlinear systems. The proposed method involves the utilization of a fractional-order fuzzy sliding mode controller, which is implemented through online fractional-order reinforcement learning (FOFSMC-FRL). First, the proposed approach leverages two Takagi–Sugeno–Kang (TSK) fuzzy neural network actors. These actors approximate both the equivalent and switch control parts of the sliding mode control. Additionally, a critic TSK fuzzy neural network is employed to approximate the value function of the reinforcement learning process. Second, the FOFSMC-FRL parameters undergo online adaptation using an innovative fractional-order Levenberg–Marquardt learning method. This adaptive mechanism allows the controller to continuously update its parameters based on the system’s behavior, optimizing its control strategy accordingly. Third, the stability and convergence of the proposed approach are rigorously examined using Lyapunov theorem. Notably, the proposed structure offers several key advantages as it does not depend on knowledge of the system dynamics, uncertainty bounds, or disturbance characteristics. Moreover, the chattering phenomenon, often associated with sliding mode control, is effectively eliminated without compromising the system’s robustness. Finally, a comparative simulation study is conducted to demonstrate the feasibility and superiority of the proposed method over other control methods. Through this comparison, the effectiveness and performance advantages of the approach are validated.

In bioinformatics, medical diagnosis models might be significantly impacted by high-dimensional data generated by high-throughput technologies. This data includes redundant or irrelevant genes, making it challenging to identify the relevant genes from such high-dimensional data. Therefore, an effective feature selection (FS) technique is crucial to mitigate dimensionality, thereby enhancing the performance and accuracy of medical diagnosis. The Cuckoo Search Algorithm (CSA) has proven effective in gene selection, demonstrating prowess in exploitation, exploration, and convergence. However, most of the current CSA-based FS techniques deal with gene selection problems as a single objective rather than adopting a multi-objective mechanism. This article proposes the Multi-Objective Binary Cuckoo Search Algorithm (MOBCSA) for gene selection. MOBCSA extends the standard CSA by incorporating multiple objectives, including accuracy of classification and number of selected genes. MOBCSA utilizes an S-shaped transfer function for transforming the algorithm’s search space from a continuous to a binary search space. MOBCSA integrates two components: an external archive to save the pareto optimal solutions attained during the search process, and an adaptive crowding distance updating mechanism integrated into the archive to maintain diversity and increase the coverage of optimal solutions. To assess MOBCSA’s performance, evaluation experiments were conducted on six benchmark biomedical datasets using three different classifiers. Then, the obtained experimental results were compared against four multi-objective-based state-of-the art FS methods. The findings prove that MOBCSA surpasses the other methods in both accuracy of classification and number of selected genes, where it has obtained an average accuracy ranging from 92.79% to 98.42% and an average number of selected genes ranging from 15.67 to 27.88 for different classifiers and datasets.

Abstract Background A major component of emergency medical care is blood. The Gaza Strip has faced repeated wars over the last few years, emphasizing the importance of blood donation even more. This study aims to assess medical students’ knowledge, attitudes, and practices regarding voluntary blood donation in Gaza (VBD). Methods This cross-sectional study used stratified sampling method to survey medical students at Gaza’s two medical schools, Al-Azhar and Islamic Universities, between March and April 2022. A 35-item self-administered questionnaire with four sections: demographics, knowledge, attitude, and practice, was used. The data were statistically analyzed using SPSS version 25. Results A total of 329 students were surveyed (response rate of 89.6%). The median age was 20 (IQR = 3). Males made up 44.7% of the sample. Overall, 54.7% were found to have good knowledge about VBD, 68.1% did not know the time-to-wait between each whole blood donation, and in terms of blood donation criteria, only 30.7%, 25.2% were aware of the appropriate age and weight for donating. Moreover, school was the source of most information (66.6%). Meanwhile, 73.3% of participants expressed a positive attitude toward VBD. Only (17,6%) did not show a willingness to donate blood regardless of their relationship with the recipient. The vast majority (83.3%) had never donated blood before, and 12.5% had no plans to do so in the future. The two most common reasons for this were the lack of opportunity and health issues (31.0%, 11.9%, respectively). Conclusion The sampled medical students had a positive attitude toward VBD, but there were deficiencies in their knowledge of blood donation criteria, and most had not donated blood. Adequate awareness campaigns are required to increase awareness about this universally and locally important subject.

The objective of this study was to evaluate the effects of soybean lecithin in broiler diets formulated with either fried or non-fried soybean oils (as an energy source), on growth performance, digestibility, and some serum parameters. A total of 600 broilers (50% male) were assigned to 4 experimental groups (with 10 replicates of 15 birds each): (i) group (S) fed with soybean oil only, (ii) group (SL) fed with soybean oil + lecithin (0.035% in feed), (iii) group (F) fed with fried soybean oil only, and iv) group (FL) fed with fried soybean oil + lecithin (0.035% in feed). Broilers that received lecithin with fried soybean oil (group FL) exhibited significantly higher body weights (1.228 vs. 1.210 kg, p

A common assumption in heat transfer models that involve fluid-solid interactions is that solid particles have a spherical shape. However, in numerous engineering applications, it is crucial to gain insights into the heat transfer mechanisms involving non-spherical particles and flowing fluids. These processes often employ particles with altered shapes, such as elongated geometries resembling prolate spheroids.A numerical study of airflow past a stationary constrained prolate spheroid under forced convective heat transfer is performed. A large temperature difference of the spheroid's surface was maintained relative to free stream temperature. Navier-Stokes and energy equations were solved to investigate the impact of Reynolds number (Re) and aspect ratio (AR) on convective heat transfer rate and Nusselt number (Nu). We focus on a wide range of spheroids' surface temperatures up to 1500 K in our study, which has never been studied in depth before. The simulations show that the mean Nu has a positive dependence on Ts, AR, and Re.A new correlation has been introduced that calculates the average Nusselt number (Nu) for spheroidal particles without assuming an isothermal condition (where the surface temperature Ts is roughly equal to the ambient temperature T∞). The suggested correlation incorporates the influence of AR, surface temperature, and Re.

Background: The prevalence of Joubert syndrome in most Arab countries is unknown, yet it has an estimated prevalence of 1/5,000 live births in the United Arab Emirates. Geographic areas where consanguinity represent high percentage (40-60%) of marriages, similar prevalence might be expected.Findings: In Case 1 (Figure 2), we found the neuroimaging features of classic JS in the form of MTS and mild vermian hypoplasia. In Case 2, (Figure 3) the imaging features included the appearance of persistent Blake’s pouch cyst. In Case 3 (Figure 4), we found the additional imaging features of Dandy Walker malformation. Case 4 (Figure 5) revealed classic JS features of the posterior fossa but with partial dysgenesis of the corpus callosum. In Case 5 (Figure 6), we found the unidentified JSRD that would help in recognizing the prognosis and clinical outcome. Case 6 (Figure 7) demonstrated the associated small posterior fossa, dysplastic superior vermis, occipital meninigocele, platybasia and compression of the cervicomedullary junction.Conclusion: First, these cases emphasize the importance of raising the clinical suspicion in this diagnosis in newborns presenting with abnormal respiration. Second, it is essential for the radiologist to look for other posterior fossa anomalies in case of finding an entity before jumping to a certain or specific syndrome. Additionally, follow-up cases of hydrocephalus should be thoroughly searched again for congenital malformations.Limitations: Our main limitation is the lack of genetic study of the presented cases due to outsourcing. That could help better classification of the Joubert syndrome in children.

Abstract Polyaniline (PANI) nanocomposites (NCs) based on cobalt (Co)-doped ZnO nanorods were fabricated (PANI-NCs) using chemical oxidation polymerization technique. Co-doped ZnO nanorods were synthesized using hydrothermal route. Microstructure characterization and UV–Visible absorption measurement confirm the formation of wurtzite ZnO nanostructured crystals. Introducing effect of Co-doped ZnO nanorods into PANI matrix on microstructural, optical, surface morphology and electrical properties of the investigated NCs were studied. The characterization of the fabricated NCs was examined using X-ray diffraction (XRD), Fourier transform infrared (FTIR), high-resolution transmission electron microscopy (HR-TEM) and four-probe DC electrical conductivity. Also, the photocatalytic activity of the fabricated NCs was examined using UV irradiation for Procion Blue dye wastewater oxidation. The photocatalytic experimental parameters were studied and the results revealed high photocatalytic activity reached to complete dye removal within 60 min of irradiation time (at pH 7.0 and room temperature). Finally, the data fitted with first-order kinetic model.

Abstract Mini abstract This work studies the direct cost of hip fractures in Egypt. The direct cost was calculated based on the incidence of hip fracture in Egypt retrieved from the national database. The result of this work raises red flags to the policy makers in Egypt that such fragility fractures are preventable, should appropriate approaches be implemented. Background This study provides an analysis for the healthcare system in Egypt. It was carried out to assess the direct annual cost incurred to the Egyptian healthcare system in 2023 as a result of fragility hip fractures in older adult Egyptians. Results The direct costs of hip fractures incurred during the first year after the injury were estimated at 1,969,385,000 Egyptian pounds (US $63,734,142.4). Time from fracture to surgery was 2.2 + 0.5 days. The average hospital stay after hip fracture surgery was 5.2 + 2.6 days. 4.5% of patients died after surgery, on average 2.3 + 0.4 months. After being discharged from the hospital, all patients needed home care. Conclusion Hip fractures have a significant clinical and financial impact on patients and the healthcare system. This study raises red flags for the healthcare policy makers in Egypt, as the financial burden due to the direct costs of hip fractures justifies extensive prevention programs for osteoporosis and fragility fractures. There is an urgent need to implement diagnostic approaches and validated management protocols for bone health disorders and its associated fractures in Egypt.

Abstract Lower respiratory tract infections caused by Streptococcus pneumoniae (Spn) are a leading cause of death globally. Here we investigate the bronchial epithelial cellular response to Spn infection on a transcriptomic, proteomic and metabolic level. We found the NAD+ salvage pathway to be dysregulated upon infection in a cell line model, primary human lung tissue and in vivo in rodents, leading to a reduced production of NAD+. Knockdown of NAD+ salvage enzymes (NAMPT, NMNAT1) increased bacterial replication. NAD+ treatment of Spn inhibited its growth while growth of other respiratory pathogens improved. Boosting NAD+ production increased NAD+ levels in immortalized and primary cells and decreased bacterial replication upon infection. NAD+ treatment of Spn dysregulated the bacterial metabolism and reduced intrabacterial ATP. Enhancing the bacterial ATP metabolism abolished the antibacterial effect of NAD+. Thus, we identified the NAD+ salvage pathway as an antibacterial pathway in Spn infections, predicting an antibacterial mechanism of NAD+.

Medication supply and storage are essential components of the medical industry and distribution. Most medications have a predetermined expiration date. When the demand is met in large quantities that exceed the actual need, this leads to the accumulation of medicines in the stores, and this leads to the expiration of the materials. If demand is too low, this will have an impact on consumer happiness and drug marketing. Therefore, it is necessary to find a way to predict the actual quantity required for the organization's needs to avoid material spoilage and storage problems. A mathematical prediction model is required to assist any management in achieving the required availability of medicines for customers and safe storage of medicines. The research question is to design a system based on deep learning that can predict the amount of drugs required with high efficiency and accuracy based on the chronology of previous years.Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, and Gated Recurrent Unit (GRU) are used to build prediction models. Those models allow for the optimization of inventory levels, thus reducing costs and potentially increasing sales. Various measures such as mean squared error (MSE), mean absolute squared error (MASE), root mean squared error (RMSE), and others are used to evaluate the prediction models. RNN model achieved the best result with MSE: 0.019 MAE: 0.102, RMSE: 0.0.

Abstract Background Radiotherapy (RT) is an essential component in the treatment regimens for many cancer patients. However, the dose escalation required to improve curative results is hindered due to the normal tissue toxicity that is induced. The introduction of radiosensitizers to RT treatment is an avenue that is currently being explored to overcome this issue. By introducing radiosensitizers into tumor sites, it is possible to preferentially enhance the local dose deposited. Gold nanoparticles (GNPs) are a potential candidate that have shown great promise in increasing the radiosensitivity of cancer cells through an enhancement in DNA damage. Furthermore, docetaxel (DTX) is a chemotherapeutic agent that arrests cells in the G2/M phase of the cell cycle, the phase most sensitive to radiation damage. We hypothesized that by incorporating DTX to GNP-enhanced radiotherapy treatment, we could further improve the radiosensitization experienced by cancer cells. To assess this strategy, we analyzed the radiotherapeutic effects on monolayer cell cultures in vitro, as well as on a mice prostate xenograft model in vivo while using clinically feasible concentrations for both GNPs and DTX. Results The introduction of DTX to GNP-enhanced radiotherapy further increased the radiotherapeutic effects experienced by cancer cells. A 38% increase in DNA double-strand breaks was observed with the combination of GNP/DTX vs GNP alone after a dose of 2 Gy was administered. In vivo results displayed significant reduction in tumor growth over a 30-day observation period with the treatment of GNP/DTX/RT when compared to GNP/RT after a single 5 Gy dose was given to mice. The treatment strategy also resulted in 100% mice survival, which was not observed for other treatment conditions. Conclusions Incorporating DTX to work in unison with GNPs and RT can increase the efficacy of RT treatment. Our study suggests that the treatment strategy could improve tumor control through local dose enhancement. As the concentrations used in this study are clinically feasible, there is potential for this strategy to be translated into clinical settings.

Abstract Due to advancements in existing Internet of Medical Things (IoMT) systems and devices, the blood glucose level (BGL) for type-1 diabetic patients (T1DPs) is effectively and continually monitored and controlled by Artificial Pancreas. Because the regulation of BGL is a very complex process, many efforts have been conducted to design a powerful and effective controller for the exogenous insulin infusion system. The main objective of this study is to propose an optimized interval type-2 fuzzy (IT2F) based controller of artificial pancreas for regulation BGL of T1DP based on IoMT. The proposed controller should avoid the risk of hyperglycemia and hypoglycemia situations that T1DP faces during the infusion of exogenous insulin. The main contribution of this work is using meta-heuristic method called grey wolf optimizer (GWO) to tune the footprint of uncertainty for IT2F’s membership functions to inject the proper dose of insulin under different conditions. The nonlinear extended Bergman minimal model (EBMM) with uncertainty is used to represent the blood glucose regulation and represent the dynamics of meal disturbance in T1DP. The effectiveness and the performance of the proposed controller are investigated using MATLAB/Simulink platform. Simulation results show that the proposed controller can avoid both severe hypoglycemia and hyperglycemia for nominal parameters of the model, in addition to model under the presence of both parametric uncertainty and uncertain meal disturbance.

This study aimed to evaluate the incidence and risk factors associated with refeeding syndrome (RFS) in preterm infants (≤32 weeks gestational age) during their first week of life. Infants (gestational age ≤ 32 weeks; birth weight < 1500 g) who were admitted to the neonatal intensive care unit (NICU), level III, and received parenteral nutrition between January 2015 and April 2024 were retrospectively evaluated. Modified log-Poisson regression with generalized linear models and a robust variance estimator was applied to adjust the relative risk of risk factors. Of the 760 infants identified, 289 (38%) developed RFS. In the multivariable regression analysis, male, intraventricular hemorrhage (IVH), and sodium phosphate significantly affected RFS. Male infants had significantly increased RFS risk (aRR1.31; 95% CI 1.08–1.59). The RFS risk was significantly higher in infants with IVH (aRR 1.71; 95% CI 1.27–2.13). However, infants who received higher sodium phosphate in their first week of life had significantly lower RFS risk (aRR 0.67; 95% 0.47–0.98). This study revealed a notable incidence of RFS among preterm infants aged ≤32 gestational weeks, with sex, IVH, and low sodium phosphate as significant risk factors. Refined RFS diagnostic criteria and targeted interventions are needed for optimal management.

Abstract The study proposes a novel model for DNA sequence classification that combines machine learning methods and a pattern-matching algorithm. This model aims to effectively categorize DNA sequences based on their features and enhance the accuracy and efficiency of DNA sequence classification. The performance of the proposed model is evaluated using various machine learning algorithms, and the results indicate that the SVM linear classifier achieves the highest accuracy and F1 score among the tested algorithms. This finding suggests that the proposed model can provide better overall performance than other algorithms in DNA sequence classification. In addition, the proposed model is compared to two suggested algorithms, namely FLPM and PAPM, and the results show that the proposed model outperforms these algorithms in terms of accuracy and efficiency. The study further explores the impact of pattern length on the accuracy and time complexity of each algorithm. The results show that as the pattern length increases, the execution time of each algorithm varies. For a pattern length of 5, SVM Linear and EFLPM have the lowest execution time of 0.0035 s. However, at a pattern length of 25, SVM Linear has the lowest execution time of 0.0012 s. The experimental results of the proposed model show that SVM Linear has the highest accuracy and F1 score among the tested algorithms. SVM Linear achieved an accuracy of 0.963 and an F1 score of 0.97, indicating that it can provide the best overall performance in DNA sequence classification. Naive Bayes also performs well with an accuracy of 0.838 and an F1 score of 0.94. The proposed model offers a valuable contribution to the field of DNA sequence analysis by providing a novel approach to pre-processing and feature extraction. The model’s potential applications include drug discovery, personalized medicine, and disease diagnosis. The study’s findings highlight the importance of considering the impact of pattern length on the accuracy and time complexity of DNA sequence classification algorithms.

High entropy alloys (HEAs) attract many researchers due to their unique and desirable properties in comparison to conventional alloys, and their potential for advanced applications. Because of the complexity of designing HEAs, several attempts have been conducted to integrate experimental and computational studies with machine learning (ML) algorithms to predict their mechanical properties. Yet, few studies have considered a set of input parameters including atomic concentrations, grain size, operating temperature, and strain rate. Therefore, this study considers these combined predictors to forecast the tensile properties of FeNiCrCoCu HEAs, including Young's modulus, yield strength, and ultimate tensile strength based on molecular dynamics (MD) and ML algorithms. 918 datasets of polycrystalline HEAs were generated by MD simulations. Some of the MD datasets were selected as representative samples and assessed by checking the isotropy of mechanical properties. Also, the MD simulations provided data that reasonably agreed with previously published results. All the generated datasets were used afterward to train Artificial neural networks (ANN), support vector machine, and Gaussian process regression models. The proposed ANN models revealed the most accurate predictions among the other ML models, and their performances were evaluated on new datasets containing different predictor variables' values that were not used to build the models. It was found that the ANN models were most sensitive to the strain rate predictor variable. The proposed ANN models can assist in guiding the experimental work to optimize the search for HEAs with desired tensile properties.

Purpose: This study aims to evaluate Doctor of Pharmacy (PharmD) students’ experience with the newly developed simulation-based pharmaceutical care (PC) rotation by evaluating their knowledge and attitudes towards PC before and after the rotation. Methods: A self-administered questionnaire was distributed to sixth year PharmD students enrolled in the clinical training rotation “Comprehensive Pharmaceutical Care” during the 2020/2021 academic semesters at Jordan University of Science and Technology’s (JUST) Faculty of Pharmacy. Questionnaires were distributed before and after completing four experiential training weeks and consisted of three sections. The first section collected students’ demographic details while the second and third sections evaluated students' knowledge about, and attitudes toward PC, respectively. Descriptive statistics were used to describe and compare changes in students' knowledge and attitudes pre-and post-rotation. Results: A total of 106 valid questionnaires were completed with a response rate of 99.07%. The rates of correct answers increased after the rotation with median total knowledge score increasing from 8 to 10 (out of 13, P value

Concrete corbels are short cantilevers subjected to monotonic and repeated loads. Repeated loads generally negatively affect the concrete structural members' strength as they decrease the resistance to external loads. To increase these loads, strengthening with carbon fiber reinforced polymer (CFRP) strips as an externally bounded technique is used. This paper studies the behavior and strength of strengthened corbels subjected to monotonic and repeated (constant and incremental) loads. The experimental program included the casting and testing of twelve double-concrete corbels. All specimens have been kept constant for corbel dimensions and main and secondary reinforcement. Nine were strengthened with CFRP strips using different patterns, while the others were left un-strengthened as control corbels. The results showed that both repeated loads' types, i.e., constant and incremental, affected the ultimate load capacity of corbels. Compared to monotonic loading, a reduction occurred in ultimate load and ultimate deflection for corbels subjected to five repeated loading cycles. For corbels strengthened by externally bounded CFRP strips under any applied loads, the ultimate load significantly increased, while the ultimate deflection decreased compared to un-strengthened at the same applied load. All corbels failed by de-bonding the CFRP strips.

One of the most significant issues in wireless sensor networks (WSNs) is security, which must be addressed to keep WSNs safe from malicious attacks. An intrusion detection system (IDS) is essential in analyzing network traffic and detecting abnormal events. However, these IDSs suffer from several drawbacks that affect their effectiveness and flexibility in accuracy, so they must overcome these drawbacks to improve the performance of IDS. These drawbacks include difficulties in determining the appropriate dataset, the problem of feature selection, and the issue of the imbalanced dataset and choosing the appropriate algorithms for the classification process in WSN. In this paper, a model for an anomaly-based IDS in WSNs is proposed. This model applied mutual information (MI) for feature selection and the synthetic minority oversampling technique (SMOTE) for solving the imbalanced dataset problem. It used different machine learning (ML) algorithms, random forest (RF), decision tree (DT), support vector machine (SVM), and K-nearest neighbors (KNNs) to analyze network traffic and binary classification or multiclass classification. To implement and evaluate the performance of the proposed model, the standard dataset NSL-KDD is used. Python language is used to implement the proposed model in the Anaconda platform, and many evaluation metrics are also utilized to evaluate the performance of the proposed method. Experimental results show that the proposed model can detect intrusions using different ML algorithms with high accuracy. The results of the proposed model for different ML algorithms outperform the state-of-the-art algorithms, and the maximum enhancement reached 15% in the accuracy metric.

BackgroundAfter its emergence in China, the coronavirus SARS-CoV-2 has swept the world, leading to global health crises with millions of deaths. COVID-19 clinical manifestations differ in severity, ranging from mild symptoms to severe disease. Although perturbation of metabolism has been reported as a part of the host response to COVID-19 infection, scarce data exist that describe stage-specific changes in host metabolites during the infection and how this could stratify patients based on severity.MethodsGiven this knowledge gap, we performed targeted metabolomics profiling and then used machine learning models and biostatistics to characterize the alteration patterns of 50 metabolites and 17 blood parameters measured in a cohort of 295 human subjects. They were categorized into healthy controls, non-severe, severe and critical groups with their outcomes. Subject's demographic and clinical data were also used in the analyses to provide more robust predictive models.ResultsThe non-severe and severe COVID-19 patients experienced the strongest changes in metabolite repertoire, whereas less intense changes occur during the critical phase. Panels of 15, 14, 2 and 2 key metabolites were identified as predictors for non-severe, severe, critical and dead patients, respectively. Specifically, arginine and malonyl methylmalonyl succinylcarnitine were significant biomarkers for the onset of COVID-19 infection and tauroursodeoxycholic acid were potential biomarkers for disease progression. Measuring blood parameters enhanced the predictive power of metabolic signatures during critical illness.ConclusionsMetabolomic signatures are distinctive for each stage of COVID-19 infection. This has great translation potential as it opens new therapeutic and diagnostic prospective based on key metabolites.

Real-time locating systems (RTLSs) suffer from clock synchronization inaccuracy among their distributed reference nodes. Conventional systems require periodic time synchronization and typically necessitate a two-way ranging (TWR) clock synchronization protocol to eliminate their measurement errors. Particularly, frequency-modulated continuous-wave (FMCW) time-based location systems pose unique design considerations on the TWR that have a significant impact on the quality of their measurements. In this paper, a valid operation design diagram is proposed for the case of an FMCW time-based TWR synchronization protocol. The proposed diagram represents an intersection area of two boundary curves that indicate the functionality of the system at a given frequency bandwidth, spectral length, and clock synchronization ambiguity. It presents an intuitive illustration of the measurement’s expected accuracy by indicating a larger intersection area for relaxed design conditions and vice versa. Furthermore, the absence of a working condition can easily be detected before proceeding with the actual system development. To demonstrate the feasibility of the proposed diagram, four scenarios with different design constraints were evaluated in a Monte-Carlo model of a basic TWR system. Moreover, an experimental measurement setup demonstrated the validity of the proposed diagram. Both the simulation and experimental outcomes show that the indicated valid conditions and the distribution of the measurements’ accuracy are in very good agreement.

AbstractConsistent with the notion that sustainability reporting (also known as corporate social responsibility (CSR) reporting) signals an ethical corporate culture and high monitoring, this study seeks to determine whether sustainability reporting reduces real earnings management (REM) practices and enhances the financial reporting quality (FRQ) in the Saudi market. The study also aims to investigate whether institutional investors impact this anticipated relationship. The study used two-stage least square (2SLS) regression, focusing on a sample comprising 840 firm-year-observations of firms listed on the Saudi Stock Exchange (Tadawul) during the 2016–2021. The empirical result shows that CSR reporting mitigates REM practice in the Saudi market. It also shows a negative link between institutional ownership and REM. Further, we document that institutional ownership strengthens the CSR-REM relationship. The study’s results remain robust even after conducting sensitivity and additional tests to address endogeneity concerns. These results are important for several users of financial reporting, such as investors, other stakeholders, auditors, financial analysts and researchers interested in understanding the level of FRQ in the Saudi market. Further, it provides a clear picture for regulatory bodies in Saudi Arabia about the current CSR reporting level, FRQ and transparency in the Saudi market that expect to help them improve regulations and rules related to these issues, which, consequently, assist in achieving the vision 2030 goals.

Monolithic zirconia and hybrid ceramic restorations have been widely used in the last decade for both anterior and posterior dental restorations. However, their use lacks sufficient scientific evidence in most cases, as the expeditious manufacturing of these versatile ceramic materials exceeds the limits of in vitro and/or in vivo validation. This study aimed to evaluate and compare the mechanical properties (flexural strength, fracture toughness, Vickers hardness, and brittleness index) of three CAD-CAM monolithic multilayer zirconia ceramics (GNX—Ceramill Zolid® Gen-X, ZCP—IPS e.max® ZirCAD, and UPC—Upcera® Esthetic Explore Prime) and one CAD-CAM monolithic multilayer polymer-infiltrated hybrid ceramic (ENM—Vita® Enamic) with a CAD-CAM monolithic lithium disilicate ceramic as a control (EMX —IPS e.max® CAD). A total of 160 discs (GNX = 32, ZCP = 32, UPC = 32, ENM = 32, and EMX = 32) were cut, polished, and fully sintered (except for the ENM). Half of the samples for each group were subjected to hydrothermal aging. Descriptive analysis and ANOVA tests were used to compare the groups. The zirconia groups showed significantly higher mechanical properties than the EMX group for both the non-aged and aged samples (p < 0.05). The ENM group showed the lowest brittleness index, while EMX showed the highest. The mechanical properties of monolithic multilayer zirconia ceramics were generally better than those of monolithic multilayer polymer-infiltrated hybrid ceramic and lithium disilicate ceramic. All groups showed, to some extent, a change in their mechanical properties after aging, with the ENM being the most affected.

The strategy for treating bladder cancer (BC) depends on whether there is muscle invasion or not, with the latter mostly treated with intravesical therapy, such as with bacillus Calmette–Guérin (BCG). However, BCG treatment is unsuccessful in 70% of patients, who are then subjected to radical cystectomy. Although immune-checkpoint inhibitors have been approved as a second-line therapy for a subset of BC patients, these have failed to meet primary endpoints in clinical trials. Thus, it is crucial to find a new treatment. The mitochondrial gatekeeper protein, the voltage-dependent anion channel 1 (VDAC1), mediates metabolic crosstalk between the mitochondria and cytosol and is involved in apoptosis. It is overexpressed in many cancer types, as shown here for BC, pointing to its significance in high-energy-demanding cancer cells. The BC cell lines UM-UC3 and HTB-5 express high VDAC1 levels compared to other cancer cell lines. VDAC1 silencing in these cells using siRNA that recognizes both human and mouse VDAC1 (si-m/hVDAC1-B) reduces cell viability, mitochondria membrane potential, and cellular ATP levels. Here, we used two BC mouse models: subcutaneous UM-UC3 cells and chemically induced BC using the carcinogen N-Butyl-N-(4-hydroxybutyl) nitrosamine (BBN). Subcutaneous UM-UC3-derived tumors treated with si-m/hVDAC1 showed inhibited tumor growth and reprogrammed metabolism, as reflected in the reduced expression of metabolism-related proteins, including Glut1, hexokinase, citrate synthase, complex-IV, and ATP synthase, suggesting reduced metabolic activity. Furthermore, si-m/hVDAC1-B reduced the expression levels of cancer-stem-cell-related proteins (cytokeratin-14, ALDH1a), modifying the tumor microenvironment, including decreased angiogenesis, extracellular matrix, tumor-associated macrophages, and inhibited epithelial–mesenchymal transition. The BBN-induced BC mouse model showed a clear carcinoma, with damaged bladder morphology and muscle-invasive tumors. Treatment with si-m/hVDAC1-B encapsulated in PLGA-PEI nanoparticles that were administered intravesically directly to the bladder showed a decreased tumor area and less bladder morphology destruction and muscle invasion. Overall, the obtained results point to the potential of si-m/hVDAC1-B as a possible therapeutic tool for treating bladder cancer.

This study delves into online assessments, focusing on their numerous advantages, including reducing paper usage and alleviating concerns surrounding the security of traditional examinations. The purpose of this study was to develop and test a model delineating the interplay between factors influencing the continuance intention to use online assessments and the influence on individual performance. A quantitative approach using an online survey was used to collect data from 222 graduate students from three major Palestinian universities. The research instrument was developed based on the finding of previous studies. Confirmatory factor analysis was employed to construct and validate the proposed model. The findings of this study revealed that perceived usefulness has a significant influence on the intention to continue using online assessment. The proposed model indicated that there is no direct or indirect relationship between perceived ease of use and the continuance intention to use online assessment. The main limitation of this study was its reliance on a purely quantitative approach. Therefore, a sequentional mixed methods approach van be a suggestion for future research and include all the higher education institutions in the Palestinian context.

Packaging bags made of polyethylene (PE) were sonochemically coated with edible antibacterial nanoparticles of chitosan (CS). In this work, the nanoparticles (NPs) were deposited on the surface of PE packaging bags by applying sonication waves on an acetic solution of chitosan. The characterization of CS NPs and PE bags was conducted by physicochemical techniques. The results showed that the coated bags had longer freshness than the uncoated ones. Furthermore, the characterization of cucumber, mushroom, and garlic placed into coated and uncoated PE bags was conducted by monitoring various parameters such as mass loss, total soluble solids, pH, and visual inspection. The study revealed that the PE bags coated with CS NPs showed a noticeable result in extending the shelf life of fresh produce. Finally, the antibacterial activity of PE bags was evaluated against various bacterial species. Hence, the PE bags coated with CS NPs could be a promising candidate for elongating the shelf life of packaged fresh produce.

Purpose: The goal of this study was to assess the potential real-world effect of the recently reported SC.24 trial on spine stereotactic body radiation therapy (SBRT) utilization. We estimated the proportion of patients treated with conventional radiation therapy (CRT) who would have been eligible for spine SBRT per trial inclusion criteria and analyzed the potential estimated increased costs to our institution. Methods and Materials: This was a retrospective review of patients who received spine CRT at our institution between August and October 2020. Data abstracted included demographics, SC.24 eligibility criteria, provider-reported pain response, and survival. A cost analysis and time survey was performed using institutional and provincial data. Results: Of 73 patients reviewed, 24 patients (33%) were eligible. The most common exclusion factors included irradiation of ≥3 consecutive spinal segments (n = 32, 44%), Eastern Cooperative Oncology Group performance status >2 (n = 17, 23%), and symptomatic spinal cord compression (n = 13, 18%). Of eligible patients, the mean age was 68.92 years, median spinal instability in neoplasia score was 8 (interquartile range, 7-9), and median Eastern Cooperative Oncology Group performance status was 2 (interquartile range, 1-2). The most common primary cancer types among eligible patients were lung (n = 10) and breast (n = 4). The median survival of eligible patients was 10 months (95% confidence interval, 4 months to not reached) with 58% surviving longer than 3 months. Of patients who had subjective pain documented after CRT, 54% had at least some response. The cost of spine SBRT was estimated at CA$4764.80 compared with $3589.10 for CRT, and tasks for spine SBRT took roughly 3 times as long as those for CRT. Conclusions: One-third of patients who received palliative spine CRT met eligibility criteria for SC.24. This possible expanded indication for spine SBRT can have a substantial effect on resource utilization. These data may be useful in guiding resource planning at institutions looking to commence a spine SBRT program.

Breast cancer is the second greatest cause of death in women worldwide, however, early detection may result in life prolongation or even complete recovery. Breast cancer can be classified by physicians into two types: benign tumors, and malignant tumors, all of which are fatal if not treated early. Several machine-learning algorithms have been developed to help physicians make diagnostic choices, concretely a convolutional neural network is presented in this paper. The proposed system is divided into several fundamental steps. The proposed classifier is trained to distinguish between incoming tumors using a dataset of 780 images. To evaluate the classifier's performance accuracy, precision, recall, and F1-score are used. In the testing stage, the proposed method achieved an overall classification accuracy of 93%, 93% precision, 93% recall, and 93% F1-score.

Collaborative filtering (CF) is one of the most widely utilised approaches in recommendation techniques. It suggests items to users based on the ratings of other users who share their preferences. Thus, one of the aims of CF is to find reliable neighbours. Typically, CF produces a sparse user-item rating matrix, when relying only on the ratings to identify the precise neighbours, resulting in poor performance. User reviews can be essential in overcoming those situations because of the diverse elements available in reviews. The most popular element is aspects, which can provide a fine-grained analysis of users’ behaviours, thus improving personalised recommendations. However, increasing the number of aspects also results in sparsity, therefore may deteriorate the recommendation performance. As a result, clustering of aspects may lessen this sparsity, but it is yet unclear how much this would affect the performance of CF systems. This study proposes a CF approach based on aspect clustering that addresses the above issue in terms of rating prediction. The approach aims to reduce the sparseness in the multi-criteria rating matrix by grouping aspects into clusters based on their semantic similarity, which will be less expensive and require less memory to discover the neighbourhood set. Our approach extracts aspects and represents them using Google’s pre-trained Word2vec model. Then, aspects are organised into clusters using the K-means clustering algorithm. Multi-dimensional Euclidean distance is used as a similarity measure for finding the appropriate neighbours and predicted ratings of unseen items are then made using the $k$ NN algorithm. This study also identifies the number of aspects that significantly impacts CF performance. Experiments are carried out using a real large-scale dataset: the Amazon movie dataset. Evaluation is also performed by comparing CF performance of the proposed approach with three different baseline approaches. Results show that the proposed approach improves CF performance compared to other approaches in terms of three predictive accuracy metrics.

Zirconia has generated tremendous interest in the esthetic materials used for dental restorations. However, using conventional zirconia has some limitations especially in highly esthetically demanding situations, as it lacks the translucency that other ceramic materials possess. This study aimed to evaluate and compare the optical properties (shade, translucency, and opalescence) of three CAD-CAM monolithic multilayer zirconia ceramics (GNX; Ceramill Zolid® Gen-X, ZCP; IPS e.max® ZirCAD, and UPC; Upcera® Esthetic Explore Prime) and one CAD-CAM monolithic multilayer polymer-infiltrated hybrid ceramic (ENM; Vita® Enamic) with a CAD-CAM monolithic lithium disilicate ceramic as control (EMX; IPS e.max® CAD). 200 discs (GNX = 40, ZCP = 40, UPC = 40, ENM = 40, and EMX = 40) were cut, polished, and fully crystallized. Half of the samples for each group were subjected to hydrothermal aging. Descriptive analysis and ANOVA tests were used to compare the groups. Zirconia GNX, ZCP, and UPC groups showed significantly lower optical properties than ENM and EMX groups for both the non-aged and aged samples (p < 0.05). UPC and ENM groups showed a significant decrease in translucency and opalescence after aging (p < 0.05). The optical properties of monolithic multilayer zirconia ceramics were lower than monolithic multilayer polymer-infiltrated hybrid ceramic and lithium disilicate ceramic. Polymer-infiltrated hybrid ceramic was significantly affected by aging.

Objectives: This study investigated science student teachers' self-esteem and their attitudes toward teaching science. Additionally, it explored the correlations between student teachers' self-esteem and various variables, such as gender, specialization, and accumulative average. Methods: The descriptive methodology was employed, and data was collected from a stratified random sample of 469 student teachers, completing their third and fourth academic years in science programs during the academic years 2020/2021. Two tools, a self-esteem scale and the attitudes toward science questionnaire, were used for data collection. Results: The findings indicated that student teachers' self-esteem and attitudes toward teaching science were moderate. Significant differences were observed in self-esteem and attitudes based on gender, accumulative average, and educational qualification. However, no significant differences were found based on the field of specialty. A significant correlational relationship between student teachers' self-esteem and attitudes toward teaching science was also established. Conclusions: The study highlights the importance of incorporating self-esteem into the professional standards for new science teachers and emphasizes the need to focus on educationally qualified graduates in science faculties who aspire to become science teachers.

The cuneiform script reveals some previously unknown aspects of our past. However, reading ancient clay tablets demands a substantial investment of time and persistent practice over a long period of time. As the fourth millennium came to a close, earlier recording methods gave way to the development of writing – the visual representation of spoken language. The first language to be transcribed in written form in Mesopotamia was Sumerian. Predominantly, the earliest tablets originate from the Uruk site in southern Mesopotamia, possibly marking its birthplace. Digitization cuneiform documents is imperative to boost research focused on the ancient Middle East. A few initiatives embarked upon this endeavor around the year 2000. Nonetheless, the digitization process is time-consuming due to the extensive volume of documents, and a dependable (semi) automatic methodology has yet to be developed. Given the antiquity of cuneiform script, recognizing cuneiform signs using real-world applications via two graph-based algorithms, each with complementary runtime characteristics, remains a manual procedure. Translating cuneiform proves to be a daunting task. Only in relatively recent times has grammar been established scientifically, while lexical challenges remain abundant and far from resolved. Furthermore, the majority of the Sumerian tablets have succumbed to the ravages of time, leaving behind only a handful of ancient depictions. Some of these old images have been preserved in a unique collection or in museums worldwide, allowing specialists to easily apply the sign detector to their cuneiform text studies. In this paper, we will discuss the categorization and analysis of clay tablets using a trained cuneiform model, employing artificial intelligence methodologies. Additionally, we will explore the methods employed, highlighting their strengths and weaknesses. Finally, we will propose potential directions for future research.

الأهداف: هدفت الدراسة إلى تعرُّف مستوى التَّنَمُّر لدى مُنتسِبات مراكز اللياقة البدنيَّة في محافظة الكرك. المنهجيَّة: اعتمدت الدراسة على المنهج الوصفي المسحي، من خلال استخدام أسلوب المسح الاجتماعي بالاستبانة. ومن أجل تحقيق هذا الهدف، صُمِّمَت استبانة لجمع البيانات، ثمَّ جرى التحقُّقُ من صدقها وثباتها، وَوُزِّعَت مِنْ ثَمَّ على عينة بلغ حجمُها (65) مُنتسِبةً من مُنتسِبات (10) مراكز لياقة بدنيَّة في المحافظة. النتائج: أظهرت النتائجُ أنَّ المتوسط الحسابي العام لمستوى التَّنَمُّر/ الضحيَّة لدى مُنتسِبات مراكز اللياقة البدنيَّة منخفضٌ، وجاء مجالُ التَّنَمُّر العاطفي/ الضحيَّة في المرتبة الأولى، تلاه في المرتبة الثانية مجالُ التَّنَمُّر اللفظي/ الضحيَّة، وفي المرتبة الثالثة جاء مجال التَّنَمُّر الجسدي/ الضحيَّة، وفي المرتبة الرابعة والأخيرة جاء مجالُ التَّنَمُّر الاجتماعي/ الضحيَّة، كما أظهرت النتائجُ وجودَ فروق ظاهريَّة بين المتوسطات الحسابيَّة لمستوى التَّنَمُّر/ الضحيَّة لدى مُنتسِبات مراكز اللياقة البدنيَّة وفقًا لمُتغيِّرات: العمر، مكان السكن، الحالة الاجتماعية، وجود الأبناء، مستوى التعليم، غيرَ أنَّ هذه الفروق ليست ذات دَلالة إحصائيَّة عند مستوى الدَّلالة (α≤0.05) في مستوى التَّنَمُّر/ الضحيَّة لدى مُنتسِبات تلك المراكز. وعليه، فقد استنتج الباحثان عددًا منَ الاستنتاجات، أهمُّها: أنَّ التَّنَمُّر الجسدي أكثرُ مجالات التَّنَمُّر سببًا في عدم الاستمرار في المؤسسات الرياضيَّة لدى الإناث. الخلاصة: قَدَّمَ الباحثان عددًا منَ التوصيات، كان من أبرزها: أن يُحافظَ على بيئة مراكز اللياقة البدنيَّة خاليةً منَ التَّنَمُّر، والتوعية بطبيعة التَّنَمُّر وعواقبه على المُتَنَمِّرِ وَالمُتَنَمَّرِ عليه (الضحيَّة)، وعدم التغاضي عن أيٍّ من أنواع التَّنَمُّر: اللفظي، والجسدي، والاجتماعي، والعاطفي.